On an aircraft, virtual sensors use a combination of data derived from physical sensors and understanding of the physics of combustion, the physics of aerodynamics and the physics of the materials involved to provide data. Machine learning is used to tune the accuracy of the output of virtual sensors.
In this way, the Digital Twin can provide precise insights into the state of a particular asset. With aircraft, this can help airlines better plan maintenance. For instance, aircraft operating in the Middle East can encounter sandy conditions. An aircraft engine blade operating in such conditions can suffer what the aviation industry calls "spallation," in which materials begin to erode from the part. Replacing the engine blade can cost $20,000, Parris notes, and more if the plane is grounded for a time because a necessary part is not immediately available.
With a Digital Twin, the airline can track the damage state for each engine blade in each jet engine.
"Every time the plane lands, we get data," Parris says. "If the level of damage is here at 2, and I need to change it when it gets to 8, I could change it at six months. Or we could decide to do a water wash on that blade. When the plane has landed on the ground at night, I could wash those blades with a solution, causing some of the dust to fall out. That water washing is expensive, but it could allow you to come in 10 months later rather than 6 months."
In the energy sector, the Digital Twin can lead to greater efficiency. For instance, wind farms deal with an issue called "wake losses." Essentially, when wind strikes a wind farm, the leading wind turbine uses the energy of the wind to turn its blades. The space behind that turbine is in its wake — the wind there is less effective at generating energy for a certain space downwind because of the turbulence created by the upwind machine.
Digital Twins can be used to measure the wind and the turbulence created by the spinning blades and adjust the speed of the blades to allow more potential energy to pass through to the downwind turbines. In this way the leading turbine produces somewhat less energy, but the downwind turbines produce more; operators can use this to maximize the energy output of their wind farms.
Bringing all this together requires deep domain expertise, Parris says.
"In the industrial space, I'm having less and less problems and yet I'm trying to predict the problems so I don't have them," he says. "I have so little data, I've got to use the domain knowledge that I have: physics knowledge, historical knowledge, insight from engineers and service workers. The only way to supplement sparse data is domain expertise."
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